2014 WordRepresentationsviaGaussianE

• (Vilnis & McCallum, 2014) ⇒ Luke Vilnis, and Andrew McCallum. (2014). “Word Representations via Gaussian Embedding.” In: CoRR, abs/1412.6623.

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Notes

Officially published as: (Vilnis & McCallum, 2015) ⇒ Luke Vilnis, and Andrew McCallum. (2015). “Word Representations via Gaussian Embedding.” In: Proceedings of the International Conference on Learning Representations (ICRL-2015).

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Word+Representations+via+Gaussian+Embedding

Quotes

Abstract

Current work in lexical distributed representations maps each word to a point vector in low-dimensional space. Mapping instead to a density provides many interesting advantages, including better capturing uncertainty about a representation and its relationships, expressing asymmetries more naturally than dot product or cosine similarity, and enabling more expressive parameterization of decision boundaries. This paper advocates for density-based distributed embeddings and presents a method for learning representations in the space of Gaussian distributions. We compare performance on various word embedding benchmarks, investigate the ability of these embeddings to model entailment and other asymmetric relationships, and explore novel properties of the representation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 WordRepresentationsviaGaussianE	Luke Vilnis Andrew McCallum			Word Representations via Gaussian Embedding						2014